The present invention is in the field of railroad track engineering and maintenance, including preventive and proactive care, and pertains more particularly to methods for aligning rail measured track data with correct geographic location of the sections from which the data was measured.
In the field of railroad engineering and maintenance, proactive maintenance of rails comprising the tracks of a railroad is extremely important for insuring safe operation of trains on the tracks. Gage widening (increase in separation between left and right rails), rail wear, fatigue-induced cracks, and other conditions have the potential to cause harmful consequences such as train derailments. Therefore, state-of-art methods are used to inspect track conditions at regular intervals along geographic sections of railroad, the sections comprising the entire length of a given line.
Special track geometry measurement vehicles known in the art as xe2x80x9cGeocarsxe2x80x9d are available to the inventor for measuring and thus enabling acquisition of important information about the condition of railway tracks along a line. Measurements that are important in proactive maintenance of a line include such as gage parameters, alignment parameters, curvature parameters, cross-level parameters, surface quality parameters, wear parameters, and so on.
Through ongoing track analysis, track degradation problems can be identified and located. By comparing old sets of data with newer sets of data along a same set of tracks, certain degradation problems can be predicted. Predicting the behavior of track degradation can be useful in planning proactive maintenance actions. Typically, analyzing and extrapolating the behavior of track measurement data taken from subsequent test vehicle runs recorded on different dates, provides maintenance authorities with information that enables one or more predictions indicative of what type of proactive maintenance should be initiated and when it should be initiated.
A requirement of the method described immediately above is that the geographic locations of measured data has to be known within a reasonable accuracy so that data from different test runs on different test dates can be compared consistently and behavior can be projected in a future sense. Some track measuring systems have geographic data provided through the use of the well-known Global Positioning System (GPS). However most existing system do not have this advantage, partly because of expense, and therefore must rely on older methods for acquiring geographic location information to aid in locating specific measured characteristics. Even with the use of GPS, geographic position results may still not be reasonable accurate for exactly pinpointing potential problems.
In the case of the systems that do not have access to GPS, the most critical problem encountered in performance of track degradation analysis is the unavailability of correct geographic location references for track measurement data recorded on different test dates. With these systems geographic location information is acquired manually by the vehicle test operator or other authorized persons by measuring distances from planted mileposts, for example. Such measurements are approximate at best. At times a geographic location assessment is made before arriving at a planned test location, or after passing the test location. An error margin of as much as 250 feet is typical in relating a geographic location to an actual test site where specific measurements were taken under such circumstances.
Other factors can cause misalignment of geographic location to test-sites, such as odometer malfunctions of a particular test vehicle and inconsistencies of odometer performance from vehicle to vehicle. For example, odometer readings are typically used to update location information for test sites. If a particular odometer of a test vehicle has a calibration error resulting in inaccurate measurement results, then the amount of error increases with distance traveled. Error in calibration can result in a standard measurement unit, for example, a foot or a meter, to be recorded longer or shorter than the actual measure. Multiplied error over distance can be as much as 50 feet misalignment in a mile or so distance. Moreover, different vehicles used to test a same length of track on different dates may have differing calibration errors, states of wheel wear, or wheel slip conditions resulting in further inconstancies. The problem can be further affected by human error. Automatic Location Detectors are available for many railroads and are used to mark and identify geographic location of track measurement data, however these detectors are often not reliably picked up by passing test vehicles and those that are detected still do not provide enough data for correct data alignment.
A system for locating a vehicle along a length of railroad track is known to the inventor and described in U.S. Pat. No. 5,791,063 hereinafter ""063. This system includes pre-measuring track geometry along the length of a railroad track and then storing this information in a historical data repository. As a vehicle moves along the same length of track having a historical geometry, the vehicle creates a real-time version of the same data and then the data is compared in order to pinpoint location of the vehicle. The described method relies on GPS positioning and previously aligned track data for reference.
A similar method is also known to the inventor and is referenced in a publication (http://ece.caeds.eng.uml.edu/Faculty/Rome/rail/trbjand,pdf) and was presented at the Sixth International Heavy Haul Railway Conferencexe2x80x94xe2x80x9cStrategies Beyond 2000xe2x80x9d, 6-10 Apr. 1997, Cape Town, South Africa. This known method uses an estimation technique based on an extended Kalman filter to recursively align track geometry data. The method and apparatus of the recursive system comprises an expensive turnkey system, which may in some embodiments also rely on GPS positioning. This method uses previously aligned data as a reference and cross-correlates new measured data against the reference or previously aligned data. It attempts to align the data using an extended Kalman filter based on the similarity of gage and cross-level signatures that are retained by the track over time. The method also requires previously aligned track data as a reference.
In light of the limitations in the prior art it has occurred to the inventor that a more economical solution is needed for finding correct geographic location of track measurement data through intelligently comparing it with track geography data that is already available in record. Track geography data information for tracks laid by a railroad is available, for example, as a part of a Roadway Information System (RIS) database and includes information such as curvature and super-elevation of curved portions of tracks.
Therefore, what is clearly needed is a method and apparatus that can be used to identify features in track measurement data that can be matched against those same features available in the track geography data of record. A system such as this could accurately locate detected problems and abnormalities in a large length of track in an automated fashion without reliance on historical alignment data or GPS positioning systems and therefore could be provided more economically and practically.
In a preferred embodiment of the present invention a computerized system for aligning measured track data collected from a length of railroad track to correct geographic location information for geometric features contained in the data is provided, comprising a first data repository containing track geography data, a second data repository containing the measured track data, and a processing component for comparing the measured track data to the track geography data. The system is characterized in that the track geography data is reconstructed to match in format and track length to the measured track data and then compared as reference data to the measured track data, the comparison made in whole and or in matching portions thereof for purpose of identifying shift in alignment between the data types, the shift relating to misalignment of geometric and geographic signatures present in both data types including shift identified as odometer error value in the measured track data, the identified shifts used to correct geometric, geographic, and odometer error misalignment in the measured track data with respect to the reference data.
In some preferred embodiments the system is maintained in and accessible from a track-geometry test vehicle and in others it is maintained externally from but accessible in part to a track-geometry test vehicle. In still other embodiments the geometric data used for alignment comprises one or a combination of curvature data, cross-level data, gage data, super-elevation data, rail twist data, and rough feature location information.
In yet other embodiments the track geography data is available from and taken from a known Railway Information System data repository. In yet others the method for comparing the measured data against the reference data is cross-correlation. In some cases the measured track data after shift correction may subsequently be used as previously aligned data for reference used in further alignment of data recorded at a later date over the same track length. In others data reconstruction of the track geography data includes data reformatting to simulate the data format of the measured track data. I still others data reconstruction of the track geography data includes segmentation to produce segments of track geography data representing data occurring over a specified track length. In still other cases shift in alignment due to odometer error is identified through linear regression.
In another aspect of the invention a method for aligning measured track data collected from a railroad track to correct geographic location information for geometric parameters in the measured track data is provided, comprising steps of (a) obtaining track geography data for use as reference data in data alignment; (b) reconstructing the track geography data to simulate in form and in coverage of length the measured track data to be aligned; (c) comparing the reconstructed reference data to the measured track data to identify a relative misalignment value between the data types; and (d) using the value identified through comparison to correct the geographic location information contained in the measured track data.
In some preferred embodiments of the method, in step (a), the track geography data is available from and taken from a known Railway Information System data repository. In other preferred embodiments, in step (a), the track geography data may contain feature location information and at least some if not all data types describing curvature data, cross-level data, gage data, and super-elevation data.
In still other embodiments of this method, in step (b), the track geography data is reconstructed to produce segments of track geography data representing data occurring over a specified track length including geometric data of features and feature location information located along the specified length. In yet other embodiments, in step (c), the method for comparison is cross-correlation and the primary parameter to be compared is curvature data. In yet other embodiments, in step (c), the method for comparison is cross-correlation and the primary parameter to be compared is super-elevation.
In yet other embodiments of this method, in step (c), the method for comparison is cross-correlation and the primary parameter to be compared is cross-level measurement. In still others, in step (c), the method for comparison is cross-correlation and the primary parameter to be compared is gage measurement. While in yet others, in step (b), the track geography data lacks curvature information of curves contained therein and the reconstruction thereof uses the ratio between super-elevation and curvature data to predict type direction and magnitude of curves. In still other embodiments, in step (b), track geography data may be divided into segments of pre-determined track lengths using a constrained optimization algorithm wherein the total length of segments not satisfying geometric constraints is minimized over a length of track for alignment consideration.
In yet another aspect of the present invention, in a data alignment process for aligning measured track data collected along a length of railroad track to a reference data set for the same length of track, a method for coarse estimation of odometer error manifest along the track length of measured track data and refining the coarse estimate to produce a final estimate used in correcting the actual odometer error manifest in the measured track data is provided, comprising steps of (a) creating a plurality of simulated data sets from the measured track data, each data set simulating a different odometer error value, each value taken at a different predetermined interval point along a predetermined maximum error range applied to the measured track data set, the range having a zero interval point at center thereof; (b) cross-correlating each of the simulated data sets against the reference data set at each interval point along the maximum range allowed obtaining a coefficient value for each of the simulated data sets; (c) identifying a single best coefficient value from those obtained in step (b) that defines a best alignment to data contained in the reference data set; and (d) repeating steps (a) through (c) using a smaller range having smaller intervals, the smaller range centered over the range interval in the first range of the measured track data associated the best coefficient identified.
In some embodiments, in step (a), the error shifts are created by shrinking the measured track data to produce shift intervals along the negative side of the range and stretching the data to produce shift intervals along the positive side of the range. In other embodiments, in step (a), shrinking the measured track data is accomplished by deleting a record from the data at uniform intervals a number of times until a desired amount of shrinking is produced and stretching the measured track data is accomplished by duplicating a record in the data at uniform intervals a number of times until a desired amount of stretching is produced. In yet other embodiments, in step (a), the maximum shift range exceeds maximum odometer error manifestation possible for the specified length of the track measured. While in still others, in step (b), the coefficient values define linear association strength between correlating interval points along the range.
In yet other embodiments, in step (c), the single coefficient value produces a coarse odometer error value. In still others, in step (d), the best coefficient found after correlating all of the simulated data sets of the smaller range intervals against the reference data set produces a final odometer error estimate for the measured track data set.
In still another aspect of the invention, in a data alignment process for aligning measured track data collected along a length of railroad track to a reference data set for the same length of track, a method for estimating a value of odometer error manifest along the track length of measured track data is provided, comprising steps of (a) cross-correlating the entire set of measured track data to the entire set of reference data to identify a relative misalignment value; (b) filtering the measured track data set to remove references to certain geometric features; (c) dividing the length of the measured and reference data sets into smaller portions; (d) cross-correlating the smaller portions of measured data against associated portions of reference data to find relative misalignment values for each portion; (e) using line regression, fitting a line through the found misalignment values plotted sequentially for each correlated data portion on a graph; and (f) determining the magnitude and direction of slope of the fitted line indicative of the magnitude and direction of the actual calibration error manifest in the measured track data.
In some embodiments of this method, in step (a), the reference data comprises previously aligned measured track data aligned to track geography data as reference data. In other embodiments, in step (a), geometric features and location information contained in both data sets are used to align the data sets. In yet others, in step (b), the geometric data references removed describe curvature data and those retained describe one or both of cross-level features and gage measurement features. In still others, in step (d), the geometric parameter for alignment is cross-level measurement.
In some cases of this method, in step (d), the geometric parameter for alignment is gage measurement, and in others, steps (a) through (f) may be carried out in batch mode using multiple measured track data sets as input and a same previously aligned data set as reference data for a same length of track, each measured track data set collected at different test runs performed at different times.